Breast cancer is the most frequent cause of cancer-related death in women worldwide.The past decades have seen a steady increase in cancer incidence, but not in mortality due to improved treatment and screening. In order to further decrease the mortality rate it is of great importance to detect breast cancer in an as early stage as possible.

To aid in the early detection of signs of breast cancer, many countries have implemented breast cancer screening programs in which women are at regular intervals invited for an examination where X-ray images of the breasts are made. Most commonly these images are mammograms (FFDM, full field digital mammography), but digital breast tomosynthesis (DBT) has been gaining much ground in the past years.

In such an examination each breast is compressed and imaged in different directions. These images are, perhaps together with earlier scans, assessed by expert radiologists for signs of breast cancer. In a screening setting, only a small proportion of the acquired images are expected to contain any suspicious signs and if there are these signs are often subtle. To aid radiologists in screening, we have been developing computed aided diagnosis (CAD) systems to improve the detection rate, and improve inter-observer homogeneity.

The state-of-the-art systems are currently based on deep learning technology, and are trained on a dataset which contains more images than a radiologist could view in their career. However, there are still many technical challenges to overcome to leverage the full amount of data available.

In this talk we will give an overview on the current state of affairs of breast cancer screening and discuss the technicalities of the modalities in more detail. The current state-of-the-art CAD systems are discussed and the engineering and scientific opportunities on improving these systems. We believe that using all data available will move us beyond the performance a single radiologist can reach, and further improve the sensitivity and specificity of our systems.